Dynamic inventory
management parameter
configuration concept
Ari Happonen
Lappeenranta University of Technology, Faculty of Technology Management, Lappeenranta, Finland
Background
•
The original problem area was in manufacturing industries. The
concept is mostly based on the know how and research work
done within this industry area
•
Could be applied to any industry with similar inventory challenges
•
The basic idea of concept:
– Keep enough inventory to keep number of exceptions on
assembly / manufacturing down, but still try to avoid
Why?
•
Manual inventory parameter handling is time consuming task
•
In case of big inventories, changing the parameters in fast
cycles is not feasible
•
On another hand, ICT based solutions can be used to
automate part of the parameter handling task
– Allows more efficient resource (time) allocation
Old problem!
•
More items on stock => longer it takes to balance the inventory parameters
compared to the demand
•
The basic reason for the problem
•
The “nature” of the demand
–
Roughly it can be said that demand is never steady / stable
–
On another words, steady demand is abnormal demand
•
Some “Solution” used by many practitioners
–
Overstock
–
Years old inventory parameters are updated only when problems arise
–
Average demand calculation + some buffer
• E.G. some sort of excel sheath models
• Usually problematic to get “correct” as demand (in general) is not
constant. Demand has changes in many dimensions, which makes
the demand look uncertainty
Demand changes / Uncertainty
Uncertainty of the demand
=> stock vs. demand management problems (failure in synchronization)
Kambil [1] defined 6 main reasons for synchronization failures
Uncertainty, ambiguity, complexity, volatility, urgency and differing
[1] Kambil, A. (2008) Strategy crossroads. Synchronization: moving beyond re-engineering. Journal of Business Strategy, Vol. 29, No. 3, pp. 51-54.
Reasons for the uncertainty
- Changes in demand
- Could be a result of changes, e.g. pricing policy change of the competitors
- Changes on the markets (new product, new working method etc.)
- Possible ICT based solutions to reactively handle these changes - Demand analysis (the profile of the item / module demand) - Periodical change analysis
Demand changes / Uncertainty
Uncertainty of the demand
=> stock vs. demand management problems (failure in synchronization)
Kambil [1] defined 6 main reasons for synchronization failures
Uncertainty, ambiguity, complexity, volatility, urgency and differing
[1] Kambil, A. (2008) Strategy crossroads. Synchronization: moving beyond re-engineering. Journal of Business Strategy, Vol. 29, No. 3, pp. 51-54.
Reasons for the uncertainty
- Changes in demand
- Could be a result of changes, e.g. pricing policy change of the competitors
- Changes on the markets (new product, new working method etc.)
- Possible ICT based solutions to reactively handle these changes - Demand analysis (the profile of the item / module demand) - Periodical change analysis
Dynamic warehouse parameter concept
•
Based on idea off demand and item classification
– Multiple step process
• Feasibility check
• Classification& item selection for automated parameter
handling
• Demand analysis
• Parameter definition and up keeping phase
•
Utilizes many different methods, but still tries to keep the
structure of the method simple enough for fast user learning
curve
Dynamic warehouse parameter concept
•
Based on idea off demand and item classification
– Multiple step process
• Feasibility check
• Classification& item selection for automated parameter
handling
• Demand analysis
• Parameter definition and up keeping phase
•
Utilizes many different methods, but still tries to keep the
structure of the method simple enough for fast user learning
curve
Feasibility check = 2 level ABC classification
2010-02-15 9 Demand € / year Critical component for manufacturing?VMI etc.
Management?
Automatic
management
Automanic / manual management Easily acquired componentsManual
management
Automatic
management
Low value items (C-class) High value items (A-class)
1. Phase ABC classification(finanzial aspect) 2. Phase classification
Manual
management
Manual
management
Manual
management
Automatic
management
Feasibility check = 2 level ABC classification
2010-02-15 10 Demand € / year Critical component for manufacturing?VMI etc.
Management?
Automatic
management
Automanic / manual management Easily acquired componentsManual
management
Automatic
management
Low value items (C-class) High value items (A-class)
1. Phase ABC classification(finanzial aspect) 2. Phase classification
Manual
management
Manual
management
Manual
management
Automatic
management
Item selection for automated parameter handling
•
Is based on feasibility study
– Industry, markets and product know how highly required
– Specially item criticality definition (e.g. for assembly etc.) is
extremely hard to define by using some software tools => manual
work required
•
Item selection for automated parameter adjustment should be made
by the people responsibly of the warehouse daily operations
– No shortcuts!
•
Result => 2 item groups
– Manually managed items
Item selection for automated parameter handling
•
Is based on feasibility study
– Industry, markets and product know how highly required
– Specially item criticality definition (e.g. for assembly etc.) is
extremely hard to define by using some software tools => manual
work required
•
Item selection for automated parameter adjustment should be made
by the people responsibly of the warehouse daily operations
– No shortcuts!
•
Result => 2 item groups
– Manually managed items
Defining the parameters
• Is based on Demand analysis
• Automatically managed items has already 3 basic
subgroups
– Defined by the 2 different ABC –analysis
dimensions
• These groups are further divided to additional
subgroups
Defining the parameters
• Demand analysis is made on two dimensions
– Change in demand amounts between different demand events – Changes in time frames between demand items
• Low changes on both => steady demand => low safety buffers needed
• Low changes on demand, high on time frame => additional buffer needed to compensate for demand “rush”
• High changes on demand, low on time frame => some additional buffers (compared to average) to keep up on longer high demand periods
• High changes on demand and time frame => too hard demand structure for any software tool?
– Can be divided on two subgroups • Low value => just use high buffers
Defining the parameters
• Demand analysis is made on two dimensions
– Change in demand amounts between different demand events – Changes in time frames between demand items
• Low changes on both => steady demand => low safety buffers needed
• Low changes on demand, high on time frame => additional buffer needed to compensate for demand “rush”
• High changes on demand, low on time frame => some additional buffers (compared to average) to keep up on longer high demand periods
• High changes on demand and time frame => too hard demand structure for any software tool?
– Can be divided on two subgroups • Low value => just use high buffers
Calculating the actual parameter values
•
Based on demand history data
– Short time frame (e.g. 2-4 moths) is used to define “resent”
market demand
– Long time frame (generally 1 year period) is used for
seasonal demand pattern analysis
•
This data is combined mathematically to predict short time
near future demand
•
DOS (Days of Supply) is used to base point for defining
Long time period
Short time period
